In response to the ever increasing demand for novel compounds useful in the effective treatment of various maladies, the medical research community has developed a number of different strategies for discovering and optimizing new therapeutic drugs. For the most part, these strategies are dependent upon molecular techniques that allow the identification of tightly binding ligands for a given biological target molecule. Once identified, these ligands may then carry out their therapeutic functions by activating, inhibiting or otherwise altering the activity of the molecular target to which they bind.
In one such strategy, new therapeutic drugs are identified by screening combinatorial libraries of synthetic small molecule compounds, determining which compound(s) have the highest probability of providing an effective therapeutic and then optimizing the therapeutic properties of the identified small molecule compound(s) by synthesizing structurally related analogs and analyzing them for binding to the target molecule (Gallop et al., J. Med. Chem. 37:1233–1251 (1994), Gordon et al., J. Med. Chem. 37:1385–1401 (1994), Czarnik and Ellman, Acc. Chem. Res. 29:112–170 (1996), Thompson and Ellman, Chem. Rev. 96:555–600 (1996) and Balkenhohl et al., Angew. Chem. Int. Ed. 35:2288–2337 (1996)). However, this process is not only time consuming and costly, it often does not provide for the successful identification of a small molecule compound having sufficient therapeutic potency for the desired application. For example, while the preparation and evaluation of combinatorial libraries of small molecules has proven somewhat useful for new drug discovery, the identification of small molecules for difficult molecular targets (e.g., such as those useful for blocking or otherwise taking part in protein-protein interactions) has not been particularly effective (Brown, Molecular Diversity 2:217–222 (1996)).
One issue that limits the success of combinatorial library approaches is that it is possible to synthesize only a very small fraction of the possible number of small molecules. For example, greater than 1060 different small molecules having valid chemical structures and molecular weights under 600 daltons can be envisioned. However, even the most ambitious of small molecule combinatorial library efforts have been able to generate libraries of only tens to hundreds of millions of different compounds for testing. Therefore, combinatorial technology allows one to test only a very small subset of the possible small molecules, thereby resulting in a high probability that the most potent small molecule compounds will be missed. Thus, suitable small molecule compounds having the required availability, activity or chemical and/or structural properties often cannot be found. Moreover, even when such small molecule compounds are available, optimization of those compounds to identify an effective therapeutic often requires the synthesis of an extremely large number of structural analogs and/or prior knowledge of the structure of the molecular target for that compound. Furthermore, screening large combinatorial libraries of potential binding compounds to identify a lead compound for optimization can be difficult and time-consuming because each and every member of the library must be tested. It is evident, therefore, that novel methods for rapidly and efficiently identifying new small molecule drug leads are needed.
Living organisms evolve through a process that includes both (1) genetic recombination, where sexual reproduction acts to mix and recombine the attributes of the parent organisms to provide progeny having attributes of both parents, and (2) natural selection, where only those progeny that are sufficiently “fit” are capable of passing their attributes on to the next generation. Approaches that closely model the process by which organism evolve have previously been reported for identifying small molecules that bind to receptors and enzymes (Weber et al., Angew. Chem. Int. Ed. Engl. 34:2280–2282 (1995) and Singh et al., J. Am. Chem Soc. 118: 1669–1676 (1996)). These approaches are based upon the mathematical method termed “genetic algorithms” (Holland, Sci. Am. 66–72 (1992)). Using genetic algorithms, a population of different compounds is screened to identify the compounds that bind to the receptor or enzyme (i.e., the “fittest” compounds). A population of progeny compounds is then prepared by recombining the building blocks that were used to prepare the “fittest” compounds. A screen is then performed to identify the compounds that bind to the target with the highest affinity, which are made up of the optimal building block combinations.
However, because the building blocks employed in the genetic algorithm approach are not preselected, one of two techniques are used to identify tight binding ligands: (1) extremely large populations of compounds must be screened and recombined, or (2) multiple rounds of screening and recombination are performed on relatively small populations where additional building blocks are gradually introduced through a process that is analogous to genetic mutation. In this second approach, many rounds of selection, recombination and building block introduction are required to identify the optimal building block combinations in analogy to the many rounds of selection, reproduction and mutation that are required in the evolution of living organisms. Thus, the use of genetic algorithms is currently limited because of the large amount of time required for compound preparation and screening, wherein the goal of new drug discovery is to identify a potent compound as quickly as possible.
Another recently reported approach for identifying high affinity ligands for molecular targets of interest is by determining structure-activity relationships from nuclear magnetic resonance analysis, i.e., “SAR by NMR” (Shuker et al., Science 274:1531–1534 (1996) and U.S. Pat. No. 5,698,401 by Fesik et al.). In this approach, the physical structure of a target protein is determined by NMR and then small molecule building blocks are identified that bind to the protein at nearby points on the protein surface. Adjacently binding small molecules are then coupled together with a linker in order to obtain compounds that bind to the target protein with higher affinity than the unlinked compounds alone. Thus, by having available the NMR structure of the target protein, the lengths of linkers for coupling two adjacently binding small molecules can be determined and small molecule ligands can be rationally designed. This approach has been useful for identifying compounds that bind to FK506 binding protein with a Kd=20 nM (Shuker et al., supra) and to stromelysin with a Kd=15 nM (Hajduk et al., J. Am. Chem. Soc. 119:5818–5827 (1997) and Hajduk et al., J. Am. Chem. Soc. 119:5828–5832 (1997)).
However, while the SAR by NMR method is powerful, it also has serious limitations. For example, the approach requires huge amounts of target protein (>200 mg) and this protein typically must be 15N-labeled so that it is useful for NMR studies. Moreover, the SAR by NMR approach usually requires that the target protein be soluble to >0.3 mM and have a molecular weight less than about 25–30 kDa. Additionally, the structure of the target protein is first resolved by NMR, a process which often can require a 6 to 12 month time commitment.
From the above, it is evident that there is a need for novel techniques useful for rapidly and efficiently identifying small molecule drug lead compounds that are capable of binding with high affinity to a molecular target of interest. We herein describe for the first time a method which is based upon pharmacophore recombination, wherein a population of small molecule pharmacophores are “pre-selected” for the ability to bind to a molecular target and wherein the small molecule pharmacophores that bind with the highest affinity are then chemically linked in various combinations to provide a library of potential high affinity binding ligands. The library of potential binding ligands is then screened using a simple functional assay for the presence of one or more compounds that bind to the target molecule with very high affinity.